Impact of different diagnostic technologies for MRSA admission screening in hospitals - a decision tree analysis

Geospatial Spread of Antimicrobial Resistance, Bacterial and Fungal Threats to Coronavirus Infectious Disease 2019 (COVID-19) Survival, and Point-of-Care Solutions

CONTEXT.—

Point-of-care testing (POCT) is inherently spatial, that is, performed where needed, and intrinsically temporal, because it accelerates decision-making. POCT efficiency and effectiveness have the potential to facilitate antimicrobial resistance (AMR) detection, decrease risks of coinfections for critically ill patients with coronavirus infectious disease 2019 (COVID-19), and improve the cost-effectiveness of health care.

OBJECTIVES.—

To assess AMR identification by using POCT, describe the United States AMR Diagnostic Challenge, and improve global standards of care for infectious diseases.

DATA SOURCES.—

PubMed, World Wide Web, and other sources were searched for papers focusing on AMR and POCT. EndNote X9.1 (Clarivate Analytics) consolidated abstracts, URLs, and PDFs representing approximately 500 articles were assessed for relevance. Panelist insights at Tri•Con 2020 in San Francisco and finalist POC technologies competing for a US $20,000,000 AMR prize are summarized.

CONCLUSIONS.—

Coinfections represent high risks for COVID-19 patients. POCT potentially will help target specific pathogens, refine choices for antimicrobial drugs, and prevent excess morbidity and mortality. POC assays that identify patterns of pathogen resistance can help tell us how infected individuals spread AMR, where geospatial hotspots are located, when delays cause death, and how to deploy preventative resources. Shared AMR data "clouds" could help reduce critical care burden during pandemics and optimize therapeutic options, similar to use of antibiograms in individual hospitals. Multidisciplinary health care personnel should learn the principles and practice of POCT, so they can meet needs with rapid diagnostic testing. The stakes are high. Antimicrobial resistance is projected to cause millions of deaths annually and cumulative financial loses in the trillions by 2050.

Core Genome Allelic Profiles of Clinical Klebsiella pneumoniae Strains Using a Random Forest Algorithm Based on Multilocus Sequence Typing Scheme for Hypervirulence Analysis

BACKGROUND

Hypervirulent Klebsiella pneumoniae (hvKP) infections can have high morbidity and mortality rates owing to their invasiveness and virulence. However, there are no effective tools or biomarkers to discriminate between hvKP and nonhypervirulent K. pneumoniae (nhvKP) strains. We aimed to use a random forest algorithm to predict hvKP based on core-genome data.

METHODS

In total, 272 K. pneumoniae strains were collected from 20 tertiary hospitals in China and divided into hvKP and nhvKP groups according to clinical criteria. Clinical data comparisons, whole-genome sequencing, virulence profile analysis, and core genome multilocus sequence typing (cgMLST) were performed. We then established a random forest predictive model based on the cgMLST scheme to prospectively identify hvKP. The random forest is an ensemble learning method that generates multiple decision trees during the training process and each decision tree will output its own prediction results corresponding to the input. The predictive ability of the model was assessed by means of area under the receiver operating characteristic curve.

RESULTS

Patients in the hvKP group were younger than those in the nhvKP group (median age, 58.0 and 68.0 years, respectively; P < .001). More patients in the hvKP group had underlying diabetes mellitus (43.1% vs 20.1%; P < .001). Clinically, carbapenem-resistant K. pneumoniae was less common in the hvKP group (4.1% vs 63.8%; P < .001), whereas the K1/K2 serotype, sequence type (ST) 23, and positive string tests were significantly higher in the hvKP group. A cgMLST-based minimal spanning tree revealed that hvKP strains were scattered sporadically within nhvKP clusters. ST23 showed greater genome diversification than did ST11, according to cgMLST-based allelic differences. Primary virulence factors (rmpA, iucA, positive string test result, and the presence of virulence plasmid pLVPK) were poor predictors of the hypervirulence phenotype. The random forest model based on the core genome allelic profile presented excellent predictive power, both in the training and validating sets (area under receiver operating characteristic curve, 0.987 and 0.999 in the training and validating sets, respectively).

CONCLUSIONS

A random forest algorithm predictive model based on the core genome allelic profiles of K. pneumoniae was accurate to identify the hypervirulent isolates.

Trends in antimicrobial susceptibility patterns in King Fahad Medical City, Riyadh, Saudi Arabia

Objectives:

To describe and interpret local antibiograms from a single tertiary care center to monitor the trends of antimicrobial resistance (AMR) patterns and establish baseline data for further surveillance.

Methods:

We performed a retrospective descriptive review of antibiograms data between January 2010 and December 2015 from King Fahad Medical City, Riyadh, Kingdom of Saudi Arabia.

Results:

A total of 51,491 isolates were identified, and most were gram-negative (76.2%). Escherichia coli was the most frequently isolated organism (36.8%), followed by Coagulase-negative Staphylococcus (28.4%) and Staphylococcus aureus (27.5%). The detection of antibiotic-resistant organisms, especially extended-spectrum beta-lactamase-producing Escherichia coli (31%-41%), increased over time. The sensitivity of Streptococcus pneumoniae to penicillin improved from 66% to 100% (p<0.001). Gram-negative isolates had excellent overall susceptibility to amikacin, variable susceptibility to piperacillin-tazobactam and carbapenems, and declining susceptibility to ceftazidime, ciprofloxacin, and cefepime.

Conclusion:

Streptococcus pneumoniae susceptibility to penicillin significantly improved over time, which might be because of the introduction of the pneumococcal vaccine. Conversely, the upward trend in resistant gram-negative organisms is worrisome and warrants the implementation of antimicrobial stewardship programs.

Nasal carriage of methicillin-resistant Staphylococcus aureus (MRSA) at a palliative care unit: A prospective single service analysis

BACKGROUND

The emergence of multidrug-resistant bacterial microorganisms is a particular challenge for the health care systems. Little is known about the occurrence of methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant Gram-negative bacteria (MDRGNB) in patients of palliative care units (PCU).

AIM

The primary aim of this study was to determine the carriage of MRSA among patients of a PCU at a German University Hospital and to assess whether the positive cases would have been detected by a risk-factor-based screening-approach.

DESIGN

Between February 2014 and January 2015 patients from our PCU were tested for MRSA carriage within 48 hours following admission irrespective of pre-existing risk factors. In addition, risk factors for MRSA colonization were assessed. Samples from the nostrils and, if applicable, from pre-existing wounds were analysed by standardized culture-based laboratory techniques for the presence of MRSA and of other bacteria and fungi. Results from swabs taken prior to admission were also recorded if available.

RESULTS

297 out of 317 patients (93.7%) fulfilled one or more MRSA screening criteria. Swabs from 299 patients were tested. The detection rate was 2.1% for MRSA. All MRSA cases would have been detected by a risk-factor-based screening-approach. Considering the detected cases and the results from swabs taken prior to admission, 4.1% of the patients (n = 13) were diagnosed with MRSA and 4.1% with MDRGNB (n = 13), including two patients with MRSA and MDRGNB (0.6%). The rate of MRSA carriage in PCU patients (4.1%) was elevated compared to the rate seen in the general cohort of patients admitted to our University Hospital (2.7%).

CONCLUSIONS

PCU patients have an increased risk to carry MRSA compared to other hospitalized patients. Although a risk factor-based screening is likely to detect all MRSA carriers amongst PCU patients, we rather recommend a universal screening to avoid the extra effort to identify the few risk factor-negative patients (<7%). As we did not perform a systematic MDRGNB screening, further studies are needed to determine the true prevalence of MDRGNB amongst PCU patients.